The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
There has been a growing interest in developing natural interactions with electronic devices that facilitate intuitiveness and enhance user experience. For instance, a user might want to control a surgical robot performing open heart surgery in another room, or a wafer processing machine in a remote clean room environment, or adjust the music volume while cooking with a free-form gesture in the air, or change the song playing on an entertainment system in the living room while cooking, or turn up the thermostat while in bed, or switch on a lamp while sitting on a couch.
Existing techniques utilize conventional motion capture approaches that rely on markers or sensors worn by the occupant while executing activities and/or on the strategic placement of numerous bulky and/or complex equipment in specialized smart home environments to capture occupant movements. Unfortunately, such systems tend to be expensive to construct. In addition, markers or sensors worn by the occupant can be cumbersome and interfere with the occupant's natural movement. Further, systems involving large amounts of hardware tend not to operate in real time due to the volume of data that needs to be analyzed and correlated. Such considerations have limited the deployment and use of motion capture technology.
Consequently, there is a need for improved techniques to capture motion of objects in real time without attaching sensors or markers thereto and to facilitate robust tracking of hands that provide inputs or perform tasks in pervasive computing environments.